FluidX3D
intel-extension-for-tensorflow
FluidX3D | intel-extension-for-tensorflow | |
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53 | 9 | |
3,210 | 302 | |
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8.6 | 9.6 | |
7 days ago | 10 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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FluidX3D
- FluidX3D
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Earthquake in Japan yesterday may have shifted land 1.3 meters
Could even use this as a revers GLOBAL ORBITING SYSTEM [GoS] - Whereby a single satellite|probe dispels a lander to a planet with the Quantum Magnetic Cannister, and that QMC signals its global location to the satellite launcher, and the satellit can extrap its location based on the absolute location of the ground guys... (might need more than one ground magnet-moles?)
How can this be measured? Can fluidx3d do martian magnetics? [0]
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[0] https://github.com/ProjectPhysX/FluidX3D
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EDIT: "Mars does not have a magnetosphere"
ELI5: "how do electronics work when there is zero magnetic field around them? A complete antimagnetic environ?"
I've never heard any mention about making any electrical device work on a planet (such as mars) in a complete magnetically dark location?
How is there gravity on mars if there is no magnetic field for a planet, and how can mass, the size of a planet not produce magnetism/gravity if its not made of iron-sh (the RED of the planet)
ELI5, please.
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Challenging projects every programmer should try
See my post in this thread about dimples/barnacles...
But have you seen this guys package: https://github.com/ProjectPhysX/FluidX3D
- Fast and Memory efficient lattice Boltzmann CFD software, running on all GPUs
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What 8x AMD Instinct MI200 GPUs can do with a combined 512GB VRAM: Bell 222 Helicopter in FluidX3D CFD - 10 Billion Cells, 75k Time Steps, 71TB vizualized - 6.4 hours compute+rendering with OpenCL
Yes, I've made that super easy. You can change the VRAM capacity of your hardware as one number in the setup script and it will automatically scale the simulation up or down. See the documentation for details.
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Need help : I' using FluidX3D, and this model is taken from the game Assetto Corsa (.kn5 file) and converted in stl using blender. In blender and many stl file viewer it shows fine but when I try to use FluidX3D it shows weird lines and I don't know why. I tried using other methods to convert t
Also see this GitHub Issue on the problem: https://github.com/ProjectPhysX/FluidX3D/issues/59
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Real time CFD with FluidX3D - Cessna 172 - 20 million cells - Titan Xp GPU
If you want to play with the software yourself, FluidX3D is on GitHub: https://github.com/ProjectPhysX/FluidX3D
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GPUs in CFD
This is my opinion about what is happening. Production CFD solvers are really complicated and take a lot of time and energy to write. Engineers learning to use a CFD solver to get a job done is really time consuming and there are all sorts of issue that come up like actually trusting the new solver. Both of these things have really held back GPUs in this area. There are really only two ways out of this, either you write a solver from scratch and get people to adopt it (double hard) or you take an existing solver and modify it to run on GPUs (still pretty hard). The first option is very hard but ultimately the way to go in my opinion. The second option results in very poorly optimized GPU code and honestly just gives a bad name to GPU computing in my opinion. Take OpenLB for example, https://www.openlb.net/show-cases/highly-resolved-nozzle-simulation-performed-using-multi-gpu-support/. Terrible terrible performance compared to what you could get if you wrote the solver from scratch on the GPU, for example, https://github.com/ProjectPhysX/FluidX3D.
- Where to try/test (and learn) CFD models for free?
- FluidX3D: Fast, memory efficient lattice Boltzmann CFD software /w OpenCL
intel-extension-for-tensorflow
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Watch out AMD: Intel Arc A580 could be the next great affordable GPU
Intel already has a working GPGPU stack, using oneAPI/SYCL.
They also have arguably pretty good OpenCL support, as well as downstream support for PyTorch and Tensorflow using their custom extensions https://github.com/intel/intel-extension-for-tensorflow and https://github.com/intel/intel-extension-for-pytorch which are actively developed and just recently brought up-to-date with upstream releases.
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How do you allocate more than 4GB of memory for OpenCL in A770 16GB?
I tried Intel® Extension for PyTorch* v1.13.10+xpu and intel-extension-for-tensorflow
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I'm really happy with the card although the Ti version offers much better performance
Yeah I recently stubbled on it when I was looking into buying a 16gb a770 and wondering what was possible now. GitHub Intel extension for tensorflow
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Does anyone uses Intel Arc A770 GPU for machine learning? [D]
Intel publish extensions for PyTorch and Tensorflow. I’ve been working with PyTorch so I just needed to follow these instructions to get everything set up.
- Intel Extension for TensorFlow
- Intel Extension for TensorFlow Released
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SD on intel arc?
Actually I was just on GitHub trying to submit issues related to me testing Intel's PyTorch and Tensorflow extensions when I saw this; it seems that someone has already ported SD over to the tensorflow framework and so you can probably start using intel's extension for tensorflow with it immediately; and according to this article you can use Intel's extension within WSL under windows as well. But unfortunately given how the guy whose issue I linked to has been facing pretty serious performance issues of inferencing taking many minutes longer than it should when using an A770 to do SD-related inferencing, you might be better off waiting for intel's extension for tensorflow versions 1.2 and greater or something like that, so that when it's your turn to use it, Intel has already ironed out most of the major bugs within the software :)
What are some alternatives?
HPX - The C++ Standard Library for Parallelism and Concurrency
stable-diffusion-tensorflow - Stable Diffusion in TensorFlow / Keras
OpenCL-examples - Simple OpenCL examples for exploiting GPU computing
intel-extension-for-pytorch - A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
McCode - The home of the McStas (neutrons) and McXtrace (x-rays) Monte-Carlo ray-tracing instrument simulation codes.
OpenCL-Wrapper - OpenCL is the most powerful programming language ever created. Yet the OpenCL C++ bindings are cumbersome and the code overhead prevents many people from getting started. I created this lightweight OpenCL-Wrapper to greatly simplify OpenCL software development with C++ while keeping functionality and performance.
pysph - A framework for Smoothed Particle Hydrodynamics in Python
compute-runtime - Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver
lbm - A simple full-python 2D lattice-boltzmann code
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
FsRayTracer - This project is an F# implementation of a raytracer based on the book "The Ray Tracer Challenge: A Test-Driven Guide to Your First 3D Renderer" by James Buck
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.